All-ferroelectric implementation of reservoir computing.

Zhiwei Chen, Wenjie Li, Zhen Fan, Shuai Dong, Yihong Chen, Minghui Qin, Min Zeng, Xubing Lu, Guofu Zhou, Xingsen Gao, Jun-Ming Liu
Author Information
  1. Zhiwei Chen: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China.
  2. Wenjie Li: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China.
  3. Zhen Fan: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China. fanzhen@m.scnu.edu.cn. ORCID
  4. Shuai Dong: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China.
  5. Yihong Chen: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China.
  6. Minghui Qin: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China.
  7. Min Zeng: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China.
  8. Xubing Lu: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China. ORCID
  9. Guofu Zhou: National Center for International Research on Green Optoelectronics, South China Normal University, 510006, Guangzhou, China.
  10. Xingsen Gao: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China. ORCID
  11. Jun-Ming Liu: Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal University, 510006, Guangzhou, China. ORCID

Abstract

Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO/SrRuO structure via the manipulation of an imprint field (E). It is shown that the volatile FD with E exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible E displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing.

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Grants

  1. 92163210, U1932125, 52172143/National Natural Science Foundation of China (National Science Foundation of China)

MeSH Term

Cognition
Long-Term Potentiation
Memory, Short-Term
Neuronal Plasticity
Nutritional Status

Word Cloud

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